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Volumn 2, Issue , 2002, Pages 660-664

Convolutional spiking neural network model for robust face detection

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPLEX NETWORKS; CONVOLUTION; INFORMATION SCIENCE; NEURAL NETWORKS; TEMPLATE MATCHING;

EID: 84964523382     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICONIP.2002.1198140     Document Type: Conference Paper
Times cited : (43)

References (13)
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  • 2
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    • Diesmann, M., Gewaltig, M.O., Aertsen, A. Stable propagation of synchronous spiking in cortical neural networks. Nature 402 (1999) 529-533
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    • Learning Invariance from Transformation Sequences
    • Földiák, P. Learning Invariance from Transformation Sequences. Neural Comput. 3 (1991) 194-200
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    • Columns for visual features of objects in monkey inferotemporal cortex
    • Fujita, I., Tanaka, K., Ito, M., Cheng, K. Columns for visual features of objects in monkey inferotemporal cortex. Nature 360 (1992) 343-346
    • (1992) Nature , vol.360 , pp. 343-346
    • Fujita, I.1    Tanaka, K.2    Ito, M.3    Cheng, K.4
  • 5
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    • Learning to generalize from single examples in the dynamic link architecture
    • Konen, W.K., and von der Malsburg, C. Learning to generalize from single examples in the dynamic link architecture. Neural Comput. 5 (1993) 1019-1030
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    • Konen, W.K.1    Von Der Malsburg, C.2
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    • Collinearity and Parallelism are Statistically Significant Second-Order Relations of Complex Cell Responses
    • Krüger, N. Collinearity and Parallelism are Statistically Significant Second-Order Relations of Complex Cell Responses. Neural Processing Lett. 8 (1998) 117-129
    • (1998) Neural Processing Lett. , vol.8 , pp. 117-129
    • Krüger, N.1
  • 7
    • 0002263996 scopus 로고
    • Convolutional networks for images, speech, and time series
    • Arbib, M.A. (ed.) MIT Press
    • Le Cun, Y., Bengio, T. Convolutional networks for images, speech, and time series. In: Arbib, M.A. (ed.) The Handbook of Brain Theory and Neural Networks. MIT Press. (1995) 255-258
    • (1995) The Handbook of Brain Theory and Neural Networks , pp. 255-258
    • Le Cun, Y.1    Bengio, T.2
  • 8
    • 0034875086 scopus 로고    scopus 로고
    • Hierarchical Pulse-coupled Neural Network Model with Temporal Coding and Emergent Feature Binding Mechanism
    • Matsugu, M.. Hierarchical Pulse-coupled Neural Network Model with Temporal Coding and Emergent Feature Binding Mechanism. Proc. International Joint Conf. On Neural Networks (2001) 802-807
    • (2001) Proc. International Joint Conf. on Neural Networks , pp. 802-807
    • Matsugu, M.1
  • 9
    • 84964537019 scopus 로고    scopus 로고
    • Object Recognition Method
    • (in Japanese) Jap. Patent No filed in 1994
    • Matsugu, M., Iijima, K. Object Recognition Method. (in Japanese) Jap. Patent No. P3078166 (2000; filed in 1994)
    • (2000)
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  • 10
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    • Detection of 3D Objects in Cluttered Scenes Using Hierarchical Eigenspace
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  • 11
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    • Precision of pulse-coupled networks of integrate-and-fire neurons
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.